to evaluate the CSI reconstruction performance. Fig. 3 compares the NMSE performance by CsiNet [1]}, CRNet-cosine [2]}, and ENet, where the performance of CsiNet and CRNet-cosine is tested under \({\bf {H}}_{\rm {s}}\) , and the performance of ENet is the average NMSE tested for both \({\bf {H}}_{\rm {R}}\)  and \({\bf {H}}_{\rm {I}}\) . From the figure, ENet outperforms CsiNet at all compression ratios and CRNet-cosine at high compression ratios. This is due to the distinct compression strategies designed upon the correlation difference. For better illustration, we visualize the reconstructed real and imaginary part in Fig. 4, where the strength of a pixel represents the magnitude of the channel values. For both \(f=16\)  and 32, ENet is able to recover the CSI in a more accurate manner than CsiNet at all compression ratios while \(f=32\)  preserves more subtle features than \(f=16\) . We also summarize the NMSE performance tested under \({\bf {H}}_{\rm {R}}\)  and \({\bf {H}}_{\rm {I}}\)  in Table II. From the table, ENet achieves similar performance for both \({\bf {H}}_{\rm {R}}\)  and \({\bf {H}}_{\rm {I}}\) , which validates the effectiveness of our finding in Theorem 1 on the correlation similarity.
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